neo4j/graph-data-science-client
Oct 2024 – Oct 2025
9 Months active
Languages Used
PythonJSONJavaScriptMarkdownadocpythonAsciiDocCypher
Technical Skills
API DesignDocumentationPythonAPI DevelopmentAPI IntegrationAPI Testing
Max Kießling
PROFILE
Max Kießling
Max Kiessling developed and maintained the neo4j/graph-data-science-client, delivering a robust suite of graph analytics features and API enhancements over nine months. He engineered new endpoints for algorithms such as Clique Counting, K-Means, Label Propagation, and Leiden, supporting both Arrow and Cypher pipelines with streaming, mutation, and write-back capabilities. His work emphasized API consistency, type safety, and maintainability, leveraging Python, Arrow Flight, and Cypher. Max improved test infrastructure, CI reliability, and code quality, addressing both feature delivery and bug fixes. The depth of his contributions enabled scalable, production-ready graph data science workflows and accelerated client adoption for data teams.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
127Total
Bugs
15
Commits
127
Features
41
Lines of code
22,937
Activity Months9
Your Network
52 people
Work History
October 2025
4 Commits • 4 Features
Oct 1, 2025October 2025 — Graph Data Science client delivered a major expansion of algorithm support and data handling capabilities across Arrow and Cypher pipelines. Implemented four core algorithms with end-to-end features such as streaming results, mutating properties, and in-database write-back, along with memory estimation and comprehensive testing. This accelerates in-database analytics, reduces data movement, and enables real-time insights through streaming outputs and robust enterprise-grade workflows.
4 Commits • 4 Features
Oct 1, 2025October 2025 — Graph Data Science client delivered a major expansion of algorithm support and data handling capabilities across Arrow and Cypher pipelines. Implemented four core algorithms with end-to-end features such as streaming results, mutating properties, and in-database write-back, along with memory estimation and comprehensive testing. This accelerates in-database analytics, reduces data movement, and enables real-time insights through streaming outputs and robust enterprise-grade workflows.
October 2025
September 2025
41 Commits • 9 Features
Sep 1, 2025September 2025 monthly summary: Expanded Graph Data Science capabilities with a broad set of new endpoints and enhancements, improved catalog exposure, and strengthened testing and CI stability. Delivered Graph Generation endpoints (abstract endpoint plus implementations for CatalogArrowEndpoints and CatalogCypherEndpoints) with a custom generate property type, introduced a Create Graph helper as a context manager and V2 Bolt test helper, and extended Node Label and Node Properties endpoints with Arrow and Cypher variants exposed via the catalog. Added Relationship endpoints with improved signatures and compatibility handling, including Arrow and Cypher implementations and catalog exposure. Strengthened end-to-end testing, streaming behavior tests, and CI reliability through targeted formatting fixes, test fixes, and CI adjustments. Extended v2 endpoint coverage for AuraGraphDataScience and SessionV2, including algorithm exposure and broader test coverage. Technologies demonstrated include Python context managers, integration and streaming tests, endpoint design and catalog exposure, and CI-driven quality improvements.
September 2025
41 Commits • 9 Features
Sep 1, 2025September 2025 monthly summary: Expanded Graph Data Science capabilities with a broad set of new endpoints and enhancements, improved catalog exposure, and strengthened testing and CI stability. Delivered Graph Generation endpoints (abstract endpoint plus implementations for CatalogArrowEndpoints and CatalogCypherEndpoints) with a custom generate property type, introduced a Create Graph helper as a context manager and V2 Bolt test helper, and extended Node Label and Node Properties endpoints with Arrow and Cypher variants exposed via the catalog. Added Relationship endpoints with improved signatures and compatibility handling, including Arrow and Cypher implementations and catalog exposure. Strengthened end-to-end testing, streaming behavior tests, and CI reliability through targeted formatting fixes, test fixes, and CI adjustments. Extended v2 endpoint coverage for AuraGraphDataScience and SessionV2, including algorithm exposure and broader test coverage. Technologies demonstrated include Python context managers, integration and streaming tests, endpoint design and catalog exposure, and CI-driven quality improvements.
August 2025
19 Commits • 4 Features
Aug 1, 2025August 2025 monthly summary for neo4j/graph-data-science-client focusing on delivering graph sampling capabilities, API consistency, and stable CI/test infrastructure. Key outcomes include feature delivery for graph sampling endpoints, catalog integration, GraphSage configuration exposure, and foundational refactors to improve API reliability. Performance and business value emphasize enabling scalable sampling workflows, faster iteration, and robust testing. Summary of deliverables: 1) Graph Sampling Endpoints and Catalog Integration: - Introduced GraphSamplingEndpoints and GraphSamplingResult with Arrow Flight and Cypher-based sampling (RWR and CNARW). - Catalog integration and snake_case naming across sampling endpoints; exposed sampling endpoints in catalog endpoints. - Implemented basic catalog functions for Cypher and native projection for Cypher. - Minor cleanups to improve maintainability. 2) Graph Sampling Endpoints Testing and Bug Fixes: - Fixed test assertions and utilities to align expected API behavior, addressing test_rwr_basic issues. 3) Test Infrastructure and CI/Networking Improvements: - Enhanced CI/test environment, Docker networking, and test configuration for local development and CI runs (env exposure, network aliases, CI skip strategies). 4) Core Catalog and Base Model Refactor: - Refactored core models: replaced GdsBaseModel with generic BaseResult to standardize API results; performed core catalog refinements. 5) GraphSage Enhancements: - Exposed configuration options for GraphSage predictions (relationship types, node labels, concurrency, batch size) via API. 6) Bug Fixes and Test Data Handling: - Fixed unit tests for JobClient/MutationClient data handling (ensuring correct data passing for retries). Overall impact: - A more consistent, scalable API foundation for graph data science features, improved test reliability and CI efficiency, and a stronger base for production-grade sampling workflows. The work emphasizes business value by enabling faster experimentation with graph sampling, safer rollouts, and easier collaboration for data science teams. Technologies/skills demonstrated: - Graph sampling endpoints design, Arrow Flight integration, Cypher-based sampling, snake_case conventions, API design, test engineering, CI/CD improvements, Docker networking, and GraphSage endpoint configurability.
19 Commits • 4 Features
Aug 1, 2025August 2025 monthly summary for neo4j/graph-data-science-client focusing on delivering graph sampling capabilities, API consistency, and stable CI/test infrastructure. Key outcomes include feature delivery for graph sampling endpoints, catalog integration, GraphSage configuration exposure, and foundational refactors to improve API reliability. Performance and business value emphasize enabling scalable sampling workflows, faster iteration, and robust testing. Summary of deliverables: 1) Graph Sampling Endpoints and Catalog Integration: - Introduced GraphSamplingEndpoints and GraphSamplingResult with Arrow Flight and Cypher-based sampling (RWR and CNARW). - Catalog integration and snake_case naming across sampling endpoints; exposed sampling endpoints in catalog endpoints. - Implemented basic catalog functions for Cypher and native projection for Cypher. - Minor cleanups to improve maintainability. 2) Graph Sampling Endpoints Testing and Bug Fixes: - Fixed test assertions and utilities to align expected API behavior, addressing test_rwr_basic issues. 3) Test Infrastructure and CI/Networking Improvements: - Enhanced CI/test environment, Docker networking, and test configuration for local development and CI runs (env exposure, network aliases, CI skip strategies). 4) Core Catalog and Base Model Refactor: - Refactored core models: replaced GdsBaseModel with generic BaseResult to standardize API results; performed core catalog refinements. 5) GraphSage Enhancements: - Exposed configuration options for GraphSage predictions (relationship types, node labels, concurrency, batch size) via API. 6) Bug Fixes and Test Data Handling: - Fixed unit tests for JobClient/MutationClient data handling (ensuring correct data passing for retries). Overall impact: - A more consistent, scalable API foundation for graph data science features, improved test reliability and CI efficiency, and a stronger base for production-grade sampling workflows. The work emphasizes business value by enabling faster experimentation with graph sampling, safer rollouts, and easier collaboration for data science teams. Technologies/skills demonstrated: - Graph sampling endpoints design, Arrow Flight integration, Cypher-based sampling, snake_case conventions, API design, test engineering, CI/CD improvements, Docker networking, and GraphSage endpoint configurability.
August 2025
July 2025
39 Commits • 16 Features
Jul 1, 2025In July 2025, the graph-data-science client matured into a more capable, production-ready platform by delivering a robust Arrow-based feature set, expanding the client ecosystem, and strengthening reliability and testing. Key work spanned core endpoint capabilities, enhanced authentication, expanded write-back capabilities, comprehensive graph-algorithm support, and improved test infrastructure. These accomplishments collectively enable faster time-to-value for graph analytics, safer production deployments, and a stronger foundation for future data-science workflows.
July 2025
39 Commits • 16 Features
Jul 1, 2025In July 2025, the graph-data-science client matured into a more capable, production-ready platform by delivering a robust Arrow-based feature set, expanding the client ecosystem, and strengthening reliability and testing. Key work spanned core endpoint capabilities, enhanced authentication, expanded write-back capabilities, comprehensive graph-algorithm support, and improved test infrastructure. These accomplishments collectively enable faster time-to-value for graph analytics, safer production deployments, and a stronger foundation for future data-science workflows.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for neo4j/graph-data-science-client: Focused on improving code quality and CI stability to deliver reliable, maintainable software for the data science client. Two key changes were delivered: (1) Code Quality: Spacing consistency around assignment operators (no functional changes) with commit 225ada7c625cee25425ebaf5371d625ad121e7ff, and (2) CI Test Stabilization: Fixed session notebook tests in CI by adjusting instance creation parameters, memory allocation, database type, and setting AURA_ENV=staging to ensure reliable notebook test runs (commit 93baf45875e8ed5149504255f5c4a93c4473cbcd). These changes reduce flaky tests, improve maintainability, and enhance overall release confidence.
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for neo4j/graph-data-science-client: Focused on improving code quality and CI stability to deliver reliable, maintainable software for the data science client. Two key changes were delivered: (1) Code Quality: Spacing consistency around assignment operators (no functional changes) with commit 225ada7c625cee25425ebaf5371d625ad121e7ff, and (2) CI Test Stabilization: Fixed session notebook tests in CI by adjusting instance creation parameters, memory allocation, database type, and setting AURA_ENV=staging to ensure reliable notebook test runs (commit 93baf45875e8ed5149504255f5c4a93c4473cbcd). These changes reduce flaky tests, improve maintainability, and enhance overall release confidence.
June 2025
May 2025
10 Commits • 3 Features
May 1, 2025May 2025 focused on stabilizing remote projections workflows and aligning client protocol with RemoteOps V3. Delivered robust QueryRunner cloning, protocol-aware remote projection handling, and correct cluster targeting for status checks; enhanced Python client typing for maintainability; and refreshed release hygiene with 1.15.1 metadata. These changes reduce connection leaks, improve correctness across multi-node projections, and strengthen downstream developer experience and release traceability.
May 2025
10 Commits • 3 Features
May 1, 2025May 2025 focused on stabilizing remote projections workflows and aligning client protocol with RemoteOps V3. Delivered robust QueryRunner cloning, protocol-aware remote projection handling, and correct cluster targeting for status checks; enhanced Python client typing for maintainability; and refreshed release hygiene with 1.15.1 metadata. These changes reduce connection leaks, improve correctness across multi-node projections, and strengthen downstream developer experience and release traceability.
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 performance month focused on stabilizing Arrow-based data transfer and aligning runtime/API with newer PyArrow versions in neo4j/graph-data-science-client; delivered incremental feature cleanup and a critical bug fix to ensure reliable data transfers and system stability.
3 Commits • 1 Features
Feb 1, 2025February 2025 performance month focused on stabilizing Arrow-based data transfer and aligning runtime/API with newer PyArrow versions in neo4j/graph-data-science-client; delivered incremental feature cleanup and a critical bug fix to ensure reliable data transfers and system stability.
February 2025
November 2024
8 Commits • 2 Features
Nov 1, 2024November 2024 highlights include expanded data access, improved client usability, and stronger test coverage for the Neo4j Graph Data Science Arrow client. Key outcomes: data retrieval enhancements via the GDS Arrow client, enabling retrieval of node properties, node labels, relationships, and relationship properties, with the ArrowQueryRunner updated to utilize the new client functions and to improve property handling and configuration robustness; added context manager support for GdsArrowClient and a configurable user agent string to improve usability, observability, and lifecycle management; expanded test coverage and stability, including tests for GDS ArrowClient actions/get methods and adaptations to arrow runner tests, along with test fixes to ensure reliability.
November 2024
8 Commits • 2 Features
Nov 1, 2024November 2024 highlights include expanded data access, improved client usability, and stronger test coverage for the Neo4j Graph Data Science Arrow client. Key outcomes: data retrieval enhancements via the GDS Arrow client, enabling retrieval of node properties, node labels, relationships, and relationship properties, with the ArrowQueryRunner updated to utilize the new client functions and to improve property handling and configuration robustness; added context manager support for GdsArrowClient and a configurable user agent string to improve usability, observability, and lifecycle management; expanded test coverage and stability, including tests for GDS ArrowClient actions/get methods and adaptations to arrow runner tests, along with test fixes to ensure reliability.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024: Delivered a focused feature enhancement for the neo4j/graph-data-science-client. Key deliverables include updated Import Command documentation with clarified parameters and return types for graph/database creation methods, as well as completion and abort actions of import processes. Introduced data classes to structure results for node, relationship, and triplet load completion operations, improving API usability, typing, and discoverability. This work accelerates client adoption, reduces onboarding time, and enhances maintainability across the library. Commit 909bed2cd493b426490eddac501953360dcf4b63—"Add documentation to import commands". Notable note: no major bugs fixed this month, but documentation and data-structure improvements deliver clear business value and technical progress.
1 Commits • 1 Features
Oct 1, 2024October 2024: Delivered a focused feature enhancement for the neo4j/graph-data-science-client. Key deliverables include updated Import Command documentation with clarified parameters and return types for graph/database creation methods, as well as completion and abort actions of import processes. Introduced data classes to structure results for node, relationship, and triplet load completion operations, improving API usability, typing, and discoverability. This work accelerates client adoption, reduces onboarding time, and enhances maintainability across the library. Commit 909bed2cd493b426490eddac501953360dcf4b63—"Add documentation to import commands". Notable note: no major bugs fixed this month, but documentation and data-structure improvements deliver clear business value and technical progress.
October 2024
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Quality Metrics
Correctness94.6%
Maintainability95.0%
Architecture92.8%
Performance83.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
AsciiDocCypherINIJSONJavaScriptMarkdownPythonShellTOMLadoc
Technical Skills
API DesignAPI DevelopmentAPI IntegrationAPI TestingAbstract Base ClassesAlgorithm ImplementationArrowArrow FlightArrow Flight ClientAsynchronous OperationsAuthenticationBackend DevelopmentCI/CDClient DevelopmentClient-Server Communication
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